A contrastive encoder trained on simulated signals maps DNA barcode signals into an interpretable molecular coordinate system. The method enables molecule identification with a single pass, reducing computational cost by three orders of magnitude and allowing data pooling across devices while remaining invariant to acquisition conditions.
A convolutional variational autoencoder trained on 6,034 Binance Options surfaces for BTC and ETH achieves 0.94-1.56 vol-point RMSE under 10-50% masking. The hybrid predictor reduces error from 7.00 to 0.83 vol points at 50% masking, outperforming parametric re-fit in structured hole patterns and detecting abnormal market events without supervision.
A new activation function enables fixed-size neural networks to approximate any function in Sobolev spaces $W^{s,\infty}((a,b)^d)$ with arbitrary accuracy in the $W^{s-1,\infty}$-norm. The results use elementary activations like EUAF and DUAF$_\infty$, with explicit width and depth bounds, and extend to sigmoidal variants $\widetilde{\mathrm{DUAF}}_n$ preserving accuracy for all $1\leq s\leq n$.
A residual learning approach using directional task-error supervision achieves stable five-ball juggling on real robots, converging from the second attempt. The system outperforms human practice timelines and relies on both directional feedback and an informative prior, with a fixed-Jacobian Newton update proving most reliable.
SPaiK introduces a scalable kernel learning method for pairwise settings using the stochastic generalized vec trick (sGVT). This innovation reduces computational and memory demands, enabling efficient training on large datasets and making pairwise kernel learning feasible for previously intractable data sizes.
A new method decouples ML inference from state persistence in streaming systems using probabilistic thinning. It selectively triggers durable state updates based on event informativeness, reducing persistence path overhead by up to 90% without compromising downstream utility or introducing systemic errors.
Dynestyx is a probabilistic programming library that provides first-class support for state-space models. It enables users to specify arbitrary priors for discrete- or continuous-time dynamical systems, perform inference on mixed-effect data, and obtain state and parameter estimates with principled uncertainty quantification.
We introduce a method to identify agents by their procedural behavior fingerprints, achieving 85.7% accuracy in attributing unseen trajectories to correct agents. Using ProcGrep, we analyze coding agent behavior in SWE-Bench, finding that models from similar release periods or distilled from each other exhibit closer behavioral similarity, with a Jensen-Shannon divergence of 0.25.
A compact spectral representation using Betti numbers, spectral gap, and analytic torsion distills persistent Laplacians into three mathematically grounded invariants. This approach captures essential predictive signals from the full spectrum, outperforms it in some cases, and reduces computational overhead on datasets like MNIST, QM-3D, and SKEMPI WT.
A new multi-center benchmark enables abdominal disease diagnosis and report generation from non-contrast CT by synthesizing contrast-enhanced findings. The dataset includes paired NCCT-CECT studies and reports from two centers, showing NCCT achieves average multi-organ AUCs of 69.1% internally and 63.1% externally. The benchmark and code are publicly released to support research into safer, contrast-free abdominal imaging workflows.
PACT combines a reactive RL policy with a 2B-parameter Small Language Model to generate and validate action plans. The SLM plan is executed directly if verified in simulation, bypassing the RL policy without retraining. PACT outperforms baselines on three increasingly difficult FrozenLake environments.
ActiveSAM is a training-free, zero-shot framework that enhances SAM 3 for open-vocabulary semantic segmentation by identifying an image-conditioned active class set. It improves speed-accuracy tradeoff, outperforming SegEarth-OV3 by +1.4 mIoU on average and running up to 5.5x faster on large-vocabulary datasets, with strong robustness to image corruption.
A measurement study finds that 26 semantic post-hoc operators do not improve held-out accuracy over Best-of-N in frozen small code models. While some operators reduce compute usage or recover correct programs, none outperform BoN in accuracy, due to systemic limitations like coverage walls and consensus traps. An expression-layer recovery (M1) improves performance on HumanEval+ by 12 tasks, with no harm or leakage, and shows consistent results across model cells.
Computing approximate stationary points of min-max optimization over the hypercube is PPAD-hard for quadratic polynomials. This result holds even for multilinear polynomials where each variable appears in at most three monomials, with inverse polynomial approximation factors. As a consequence, two-team zero-sum polymatrix games are proven to be PPAD-hard.
TuneJury is an open, instance-level pairwise reward model that predicts music preference scores from text prompts and audio clips. It is trained on diverse human-preference data and demonstrates strong generalization, with anchor calibration enabling efficient post-hoc alignment for music generation systems.
NEXIS identifies causal heterogeneous treatment effects by discovering Markov-blankets in pre-treatment data. It leverages multi-modal, multi-view measurements and scalable representations with minimal human input, enabling interpretable and actionable policy insights from controlled experiments.
ROVE enables humanoid Vision-Language-Action models to learn effective manipulation behaviors using imperfect human interventions. It combines a human-in-the-loop data collection pipeline with Optimistic Value Estimation and cross-embodiment supervision to prioritize high-value actions and improve robustness. ROVE outperforms baseline methods on real-world, contact-rich manipulation tasks through iterative rollout and intervention cycles.
A study uses multiplicative ergodic theory to analyze exploding and vanishing gradients in deep neural networks. It shows that residual connections affect the Liapunov spectrum, as characterized by Furstenberg and Kifer, thereby stabilizing gradient flow during training.
A new method called filtered conformal ellipsoids provides prediction sets for multivariate time series by using a frozen state-space filter to generate predictive means and covariances, then applying split-conformal calibration to Mahalanobis scores. The approach achieves coverage under dependence through contraction in an observable predictive-law quotient, with theoretical bounds derived under Gaussian-projection and observability conditions, and shows sharper ellipsoids on graph-native traffic benchmarks compared to static and non-filter baselines.
TokenPilot reduces inference costs by 61% to 87% in both isolated and continuous modes, outperforming prior systems in cost efficiency while maintaining competitive performance. It uses ingestion-aware compaction and lifecycle-aware eviction to stabilize prompt prefixes and manage context segments efficiently.